The most important big data analytics trends to see in 2019

Currently, users are still on their way of finding more big data for a lot of purposes and the most highlighted reason is to develop the digital revolution. Nevertheless, making such huge amounts of data into useful insights could be a very complicated task. And companies and organizations that are ready to face with and solve the difficulties of big data will get a lot of benefits in finance due to the development of digitalization.

This article is going to present the most important trends of big data that companies should prepare for this year.

1. Managing data is not easier.

The big concept lying behind big data analytics is about seeking for various patterns inside very large amounts of data, setting machine learning models to detect the patterns of data, making those models into the process of production. Then repeat if it is necessary.

Nevertheless, the concept above is much more difficult than saying. When it comes to beginners, collecting data from a lot of silos seems to be so hard as it asks them to have both ETL and database skills. What is more, it also takes a lot of effort and time to cleaning as well as labeling the data for machine learning. The case gets more serious in terms of deep learning methods. Last but not least, sending such a complicated system into production at a large scale securely also asks for a lot of professional skills.

As the reasons listed above, managing data is actually a really big challenge that requires data specialists to advance their knowledge.

2. Data silos will keep on proliferating

This prediction seems to be rather obvious. When Hadoop made a big leap five years ago, people are leading to a new initiative: the ability to consolidate all of their data onto only one single platform.

On the contrary, the biggest challenge on this idea is that there are so many data types with various storage demands. Everything from relational database to object stores has their own strengths and drawbacks. And developers are not able to maximize all the strengths if they have to take all the data into only one data lake.

To some extent, transferring so much data into only one single place is so important. Such cloud data storages as S3 are on their way of offering businesses flexible and affordable storage and Hadoop is still considered to be the most economical one for unstructured data storage and analytics. However, other companies still have a lot of silos to control. Without a centralized force with high strength, data silos will keep on proliferating. Users must get used to it.

3. A big year for streaming analytics

It is true that your organization will be better and more effective if you can deal with the new piece of data more quickly. This is regarded as a driving motivation lying behind streaming analytics. In this case, the big difficulty here is that it is harder and costly to pull off, yet this is for good reason that It teams of organizations will mature and can develop their companies more effectively. Together with SQL abilities, such open source frameworks as Kafka, Spark and Flink , we are going to witness a new progress in 2019.

4. Governing data to create steam

Some people regard data as the new oil or the new currency. No matter what terms are used, it is internationally recognized that data brings about value but if it is done carelessly, risks will come.

According to European Union, there are a lot of problems related to finance if data is governed badly. In addition to bad results related to finance, there are also consequences caused by data breaches. From an online survey, it is estimated that about 60 million people in America have been badly influenced by identity attack in the previous year. This number is more than 300 percent higher compared to 2017, when only 15 million of them claimed that they were affected. In this case, we had better govern data carefully and effectively to deal with these challenges. Organization managers should alert their staff about letting sensitive data be exposed to public, which is really dangerous for their operations.

5. Skills must be changed when technology continues developing

In a big data project, money is much spent on human resources, an indispensable part of all companies. In other words, people are mainly responsible for setting and operating data in company operation. It is really vital to employ the right person with enough required skills in order to turn data into valuable insights regardless of what technologies they need to use.

However, the case is more difficult as technology is being developed day by day. Thus, people need to have more professional skills. For instance, in this year, you can see that companies will be always looking for someone being able to put a neutral network in production. Moreover, among many programming languages, Python is still the most popular one.

Furthermore, IT engineers who are able to deal with such major tools as Spark, databases and so on will have more chances in career in this year. People also realize that demand for those who can work with machine learning will go up. As new trends turn up, new demands are needed as well.

On the contrary, due to the development of data science platforms which are automatic, companies are now capable of completing, to some extent, with mere data specialists. To sum up, knowledge of the data and the entrepreneurs will be widened further as the big data road is longer.

6. More deeper sights for deep learning

The last trend to see in this year is deep learning. In this year, companies will keep on making new trials with deep learning frameworks such as Caffe, Keras and so on due to the need of monetizing very huge amounts of data sets.